# Sixtyfour — full text

> The substance of https://www.sixtyfour.ai as one document: what the
> product is, the published benchmark results in full, and every article,
> inline. Generated at build time from the same sources the pages use.

> Generated: 2026-09-09

---

## What Sixtyfour is

Sixtyfour is an identity intelligence platform. It deploys AI research
agents that investigate a person or company starting from a single
identifier — an email address, a name, a phone number, a company or a
domain. The agents browse the open web and the dark web, official records
and unstructured sources, cross-reference what they find, and resolve it
into one profile in which every claim is cited back to the record it came
from.

The company is Sixtyfour AI Inc., based in San Francisco and backed by
Y Combinator. It is SOC 2 compliant.

### How it works

1. **Start with any identifier.** Begin with an email, name, phone number, company, or any other known data point.
2. **Agents research every source.** AI agents autonomously browse, extract, and cross-reference across the open and dark web, official records, and unstructured sources.
3. **Source-backed evidence, delivered.** Every verified source, record, and relationship is resolved into one cited intelligence profile.

### Who uses it

- **Research API** — a self-serve research API that powers some of the leading data products in the world.
- **Trust and safety** — sellers, hosts, and merchants at onboarding and at volume, and the rings operating behind them.
- **Investigations** — threat actors, counterparties, and case subjects. One identifier to full footprint.
- **Workforce identity** — candidates and contractors who cleared every verification check and still are not who they claim to be.
- **Financial crime** — enhanced due diligence, adverse media, and alert triage with sourcing that holds up in an exam.

Demo requests: https://www.sixtyfour.ai/demo. API documentation: https://docs.sixtyfour.ai/introduction

---

## Trust and safety

Source: https://www.sixtyfour.ai/solutions/trust-and-safety

Account intelligence for trust and safety teams.

Traditional fraud tools capture what happens on your platform: device
fingerprints, IP addresses, behavioural patterns, payment signals. They are
good at catching known bad actors from shared signal networks, and they work
once a pattern is established. Teams catch some operators that way, but the
sophisticated ones have already built around those checks.

Your tools can tell you a device showed up. They cannot tell you who is
behind it. The identity behind the account exists across the entire
internet, and that is where Sixtyfour looks — aliases, dark web, archived
and deleted content, and cross-platform handles, rather than the IP address,
device fingerprint, surface email and on-platform behaviour a platform can
see by itself.

### What trust and safety teams use it for

- **Financial crime and KYC/KYB** — company profiles resolved against registry, web presence, team and press signals.
- **Marketplace risk and seller integrity** — a seller listing counterfeit goods usually operates on several platforms under similar handles; the agents cross-reference them.
- **Workforce integrity and verification** — candidates and contractors whose submitted credentials a team cannot fully verify, checked against LinkedIn, university, GitHub and press records.
- **Platform abuse** — a new account matched to a previously banned one through username, shared device fingerprint, forum activity and behavioural pattern.

---

## RECON — a benchmark for people intelligence

Source: https://www.sixtyfour.ai/recon-benchmark · HUD results snapshot updated September 7, 2026. This is the snapshot date, not the date of every run.

RECON measures how accurately AI systems find and verify facts about real
individuals across scattered, unindexed and often conflicting sources.

- 140 individuals, chosen across industries, roles and digital-footprint levels, avoiding public figures whose information is too widely indexed to stress-test the benchmark.
- 514 human-verified fields. Every field was verified against a primary source with an unambiguous identity link to the person; fields relying on inference were rejected.
- 14 systems evaluated.
- Scoring is per-field against ground truth, judged by an LLM at 98.5% agreement with human raters.

Metrics: **weighted accuracy** (correct minus wrong, divided by all fields),
**accuracy** (share of fields answered correctly), **precision**
(share of attempted answers that were correct), **recall** (share of the
field set answered correctly). Earlier p50 latencies are historical reference only.
Latency reference: June 16–17, 2026, not the HUD runs.

### Full results

| # | System | Tier / provider | Weighted accuracy | Accuracy | Precision | Recall | Wrong | Earlier p50 (June 2026) |
|---|---|---|---:|---:|---:|---:|---:|---:|
| 1 | Sixtyfour High | High | 54.3% | 67.7% | 83.5% | 67.7% | 13.4% | 459s |
| 2 | Sixtyfour Medium | Medium | 44.7% | 56.0% | 83.2% | 56.0% | 11.3% | 223s |
| 3 | Parallel Ultra 2x | Parallel | 41.1% | 54.3% | 80.4% | 54.3% | 13.2% | 834s |
| 4 | Parallel Ultra 8x | Parallel | 37.5% | 52.1% | 78.1% | 52.1% | 14.6% | 678s |
| 5 | Grok 4.20-ma | xAI | 36.2% | 51.6% | 77.0% | 51.6% | 15.4% | — |
| 6 | Grok 4.6 | xAI | 36.0% | 50.8% | 77.4% | 50.8% | 14.8% | — |
| 7 | Grok 4.3 | xAI | 30.9% | 44.9% | 76.2% | 44.9% | 14.0% | 15s |
| 8 | Sixtyfour Low | Low | 28.8% | 49.8% | 70.3% | 49.8% | 21.0% | 230s |
| 9 | Parallel Ultra | Parallel | 27.2% | 43.4% | 72.9% | 43.4% | 16.1% | 589s |
| 10 | Exa agent xhigh | Exa | 23.9% | 37.7% | 73.2% | 37.7% | 13.8% | — |
| 11 | GPT-5.6-sol xhigh | OpenAI | 20.4% | 31.3% | 74.2% | 31.3% | 10.9% | — |
| 12 | Gemini 3.1 Pro (high) | Google | 13.4% | 23.2% | 70.4% | 23.2% | 9.7% | 87s |
| 13 | DeepSeek V4 Pro (high)* | DeepSeek | 9.3% | 12.6% | 79.3% | 12.6% | 3.3% | — |
| 14 | Claude Haiku 4.5 | Anthropic | 1.6% | 7.6% | 55.7% | 7.6% | 6.0% | — |

*DeepSeek counts (65 correct, 17 wrong, 432 missing) are uniquely inferred from rounded weighted accuracy and precision at 514 fields, not a raw export.
Additional supplied results are unranked pending verification of their incomplete denominators:
- Claude Opus 5: 49 correct / 39 wrong / 414 missing (502 total).
- Claude Fable 5: 14 correct / 9 wrong / 421 missing (444 total).
- Claude Sonnet 5: 26 correct / 27 wrong / 440 missing (493 total).
[Historical April results, on a different dataset](https://www.sixtyfour.ai/research/recon/2026-04-results.json).

---

## Articles

### How We Use Agents for Reverse Username Search Investigations

*Bad actors hide behind pixelated avatars and disposable handles. Here is how the chain unravels.*
Source: https://www.sixtyfour.ai/blog/how-we-use-agents-for-reverse-username-search-investigations
Hashim Khawaja · 2026-05-16 · Trust & Safety

#### Field Brief

- Scams starting on social media accounted for the highest total losses at $1.4 billion in 2023.

- Trust and safety teams rely on IP bans or device fingerprinting, but operators rotate hardware and VPNs.

- Our agents can automate a reverse username search, tracking handle reuse across forgotten forums, dormant Discord servers, and old data to surface real names, locations, and emails in minutes.

#### The Anatomy of a Reverse Username Search

On platforms like Roblox, which has 78 million daily active users, more than 40 percent of the user base consists of preteens. Because of strict compliance rules, gaming platforms cannot ask children for their real name, email, or phone number at signup. Every user is an anonymous username and a pixelated avatar.

Since 2018, only 24 people have been arrested for abusing children they met on that specific platform. The reason predators evade capture is simple. Trust and safety teams ban the IP address. The operator boots up a residential proxy. The platform bans the account. The operator registers a new one.

But a username is rarely single-use. The internet is sticky. An operator spinning up a burner account on a gaming platform in 2024 shares behavioral DNA with an account they created on a defunct forum in 2018. They reuse passwords. They leak their own aliases.

This behavior scales across organized fraud rings. Account takeover attacks jumped 354% year-over-year in Q2 2023 across Sift's global network.

A manual reverse username search relies on an analyst plugging that handle into search engines, deep web databases, and known operator registries. The analyst hopes for a match. It works, but it takes days or weeks of cross-referencing. By the time the analyst links a gaming handle to a known fraud ring, the operator has already extracted the funds or moved to a new target.

#### Resolving the Alias to a Real Jurisdiction

Finding a real name is only the first phase. The final step of a reverse username search is resolving that identity to a physical jurisdiction and mapping the associated corporate entities.

This is highly relevant in marketplace fraud and intellectual property theft. In 2022, federal authorities cracked a massive counterfeit hardware ring. Onur Aksoy, who used the alias “Dave Durden,” ran a massive scheme trafficking over $1 billion in counterfeit Cisco gear through at least 19 companies and dozens of online storefronts.

Tracing an alias like “Dave Durden” manually through Amazon seller pages and eBay profiles yields fragmented data. The operator registers storefronts under synthetic identities.

> Check out social media, Telegram and everywhere else. The proliferation of advertisements that lure consumers into this rabbit hole of fraud is just getting started. Buckle up. — Frank McKenna, Chief Fraud Strategist, Point Predictive

Our agents follow that trail to surface a real name, location, email, and full digital profile from a handle alone. They automate the exact methodology a forensic accountant or federal investigator uses, executing the queries in parallel across thousands of disparate data sources.

Instead of a trust and safety team waiting 31 days for a subpoena response to identify a seller, they see the operator's real name and associated shell companies the moment the account flags for suspicious volume. They identify the operator before the funds leave the platform.

If our agents can find a predator hiding behind a Roblox username, imagine what the graph can do when you need to research a syndicate targeting your onboarding flow.

Full integration docs at docs.sixtyfour.ai.

---

### How We Use Agents for Identity Resolution on Banned Scammers

*Device fingerprinting works on the bottom 90% of operators, but the top 10% rotate hardware before breakfast. Here is how to stop them.*
Source: https://www.sixtyfour.ai/blog/how-we-use-agents-for-identity-resolution-on-banned-scammers
Saarth Shah · 2026-05-12 · Trust & Safety

#### Field Brief

- Sift recorded a 354% year-over-year increase in account takeover attacks, driven largely by operators bypassing traditional device fingerprints.

- Most trust and safety teams respond by banning the account, but banned scammers return within hours using clean IPs and fresh emails, creating the empty footprint anomaly.

- In theory, this can be solved by tracing a single banned username back through old forums, social accounts, and breach data to find the real person.

- Our agents do exactly that, cross referencing clear web public records, underground marketplaces, and proprietary databases to map the human behind the keyboard.

#### The Account Takeover and Ban Evasion Loop

Sift recorded a 354% year-over-year increase in account takeover attacks in Q2 2023. Trust and safety teams ban these compromised accounts daily. The operators simply return the next morning with a clean IP and fresh email address.

This creates the empty footprint anomaly. We define this as a signup method with zero historical presence across breaches, social platforms, or public records. A real person has years of digital depth tied to their primary email. They ordered food, bought shoes, and registered for forums. A returning bad actor has an account created 12 hours ago.

> What's driving a majority of the fraud risk in the last 12 to 18 months has been the sharing of schemes like credit washing and stolen social security numbers on social media. — Frank McKenna, Chief Fraud Strategist at Point Predictive

If a platform relies purely on session telemetry, the attacker already has the playbook to bypass it.

#### How to Map Cross Platform Fraud Rings

The FBI's Internet Crime Complaint Center reported $12.5 billion in losses across 880,418 complaints in 2023. A massive portion of that volume flows through repeat offenders using burner identities. Real investigations prove that isolated bans do not disrupt these networks. Identity resolution proves exactly how these networks unravel.

#### Tracing the Reused Username

A marketplace customer banned a top scammer. Twelve hours later, a new applicant appeared. The only mistake the operator made was reusing the same username across niche forums even when he rotated primary emails.

The banned username appeared on seven different platforms. One of those matches traced back to a 2016 gaming forum. The operator had posted there using his real first name. That single post connected to a Reddit account listing his home city. The Reddit account led directly to an email exposed in a 2021 breach data dump. That breach record contained his full legal name. The operator thought he was anonymous. He was actually leaving a traceable map.

#### How Identity Resolution Works

We do not rely on session telemetry or IP geofencing to map these networks. We built an investigative layer that cross references fragmented signals across the internet.

##### The Input Layer

Investigators input a single identifier into the platform. This can be an email, a phone number, a Telegram handle, a cryptocurrency wallet, or a marketplace seller ID.

Our agents query multiple surfaces in parallel. The system searches Sixtyfour proprietary identity databases, dark web underground mentions, and historical breach records detailing password reuse. It maps social profiles across platforms like LinkedIn, GitHub, Reddit, Steam, and Discord. It pulls clear web public records, including LLC registries, court filings, and marketplace seller pages.

##### Resolving the Graph

LLM reasoning weighs the quality of these signals. A phone number surfacing in a 2023 breach record tied to an email represents a high confidence link. That same email appearing in two account rental Telegram groups forms another edge.

The output is a clear graph. It shows the connected accounts, the confidence scores for each edge, and the exact platforms the operator uses.

#### What This Means for Trust and Safety Stacks

Trust and safety teams must adapt their entry gates. Device fingerprinting catches the operator who reuses one phone. It does not catch the operator who buys a $30 burner SIM for each new account.

Teams should monitor for the empty footprint anomaly first. It catches the highest confidence synthetic accounts immediately at onboarding. An email with zero history on GitHub, Reddit, or historical breaches is rarely a high value customer.

In 2023, FinCEN issued advisories explicitly noting that state sponsored actors, including the Lazarus Group, exploit weak onboarding checks to launder funds. Stopping these actors requires looking past the device.

Second, teams must implement identity resolution on banned operators to understand the blast radius. If you do not map the entire network, banning one account just forces the operator to switch tabs. The goal is removing the human behind the keyboard, not just burning their current alias.

---

### How We Use Agents to Detect Rented Gig-Worker Accounts

*A $65 Facebook transaction is all it takes to put an unvetted stranger behind the wheel. Here's how Sixtyfour closes that gap.*
Source: https://www.sixtyfour.ai/blog/how-we-use-agents-to-detect-rented-gig-worker-accounts
Hashim Khawaja · 2026-05-08 · Trust & Safety

The app said a woman was delivering her food. A man showed up. He had paid $65 for that account on a Facebook group. This is what a verification gap looks like in the real world.

#### The Problem

- The February 2025 Wilbraham, MA Uber Eats assault was committed by a man using a rented account registered to a woman.

- 1 in 4 gig workers has rented or sold their verified account; this rises to nearly 1 in 3 for Millennial and Gen Z drivers.

- Standard KYC and background checks at signup miss this completely. The problem is a failure of continuous identity verification, not onboarding.

#### The Gig Economy's Continuous Identity Problem

Every major gig platform runs background checks. The accounts are legitimate. The people who passed are real. The issue is what happens after verification.

These accounts are openly traded in private Facebook groups and Telegram channels for anywhere from $65 to over $400 a month. The platform recognizes a valid account ID, but the trust-and-safety team is unaware of the transaction that put an unvetted person behind the wheel.

#### How We Trace the Real Person

##### The Input: A Flagged Account

An investigation starts with a driver account for "Sarah L." Flagged for a sudden spike in negative reviews and trips running 18 hours a day. The only hard data point: her phone number.

##### The First Trace: Anchoring the Real Owner

Our agent cross-references the phone number against our identity graph, breach data, and public social profiles. Within seconds: LinkedIn, GitHub, forum usernames, and two breach records — all linking back to the same person.

##### The Second Trace: Surfacing the Renter

The device ID logging trips is an Android model Sarah has never used. Trip origins are 60 miles from her address. Active hours don't align with her social posts. Our agent finds the renter on a dark web forum using a password fragment from one of Sarah's old breaches.

#### How Identity Resolution Differs From Standard KYC

Traditional KYC answers one question: is this person who they say they are right now? Our agents answer a different one: who is this person, everywhere?

1. The Input — A single flagged identifier: email, phone, username, crypto wallet, or name.

2. The Search — Cross-referenced across Sixtyfour's proprietary databases, dark web sources, breach records, social profiles, and public records.

3. The Resolution — An LLM-driven inference layer weighs signal quality and maps resolved entities into a unified graph.

4. The Output — A graph of connected accounts with confidence scores. Not a single data point — a complete picture.

5. What We Don't Do — No IP-based ID, no device fingerprinting, no private platform data, no legal determinations.

---

### How We Trace Hidden Beneficial Owners Behind Shell Companies

*In 2024, TD Bank pleaded guilty to $18.3 trillion in unmonitored transaction activity tied to money laundering. Here is how agents trace the beneficial owners behind shell networks.*
Source: https://www.sixtyfour.ai/blog/how-we-trace-hidden-beneficial-owners-behind-shell-companies
Roham Mehrabi · 2026-05-05 · Trust & Safety

In 2024, TD Bank pleaded guilty to $18.3 trillion in unmonitored transaction activity tied to money laundering, and here is how to trace the beneficial owners behind the shell networks using agents.

#### Field Brief

- FinCEN reported the United States loses approximately $70 billion annually to shell company exploitation, masking illicit funds behind layered LLCs and nominee directors.

- Most compliance teams pull the corporate registration, find a law firm address or nominee name, and hit a dead end when the real owner does not appear on the paper.

- Stopping the flow requires tracing the digital footprint of the formation email, the shared registered agent, and cross-platform public records to find the human controller.

- We use our agents to cross reference entity names against breach data, court filings, and public registries to map the hidden human network in minutes.

#### The AML Compliance Gap in Shell Company Detection

In 2024, the United States Department of Justice forced TD Bank to plead guilty to Bank Secrecy Act violations. The institution failed to monitor $18.3 trillion in transaction activity over six years. Much of this unmonitored flow moved through layered corporate entities designed specifically to obscure the beneficial owner.

We call the primary evasion tactic the shell-LLC chain. We define this as multiple LLCs registered to the same agent address, identifying coordinated networks rather than isolated businesses. A second pattern is nominee shielding, where an operator uses a localized registered attorney to sign formation documents while the controller remains completely off the record.

> The current patchwork of AML/CFT requirements creates regulatory gaps that criminals and foreign adversaries exploit to launder money, hide illicit wealth, and compromise American innovation. — Andrea Gacki, Director at FinCEN

#### How to Map Beneficial Owners in AML Investigations

Consider the DOJ findings in the Onur Aksoy counterfeit ring. The operator controlled at least 15 different shell entities. Each entity shielded a portion of the fraudulent Cisco network from direct attribution. When investigators shut down one LLC, another absorbed the volume immediately.

The human operator never used his real name on the initial Amazon seller applications. Instead, investigators found an email address tied to a formation document. That email had surfaced in a 2018 data breach. Cross referencing the compromised email pulled up a specific burner phone number.

That same phone number appeared on a completely separate domain registration two years prior. The domain registration included a physical address. That physical address matched the registered agent for four additional LLCs operating in the exact same marketplace.

The network collapses when you map the human history behind the entity. A shell company is an abstract legal wrapper, but a human operator leaves a permanent digital footprint.

#### Finding the Real Person Behind a Shell LLC in 2026

Here is how our agents resolve the network. The input is a single piece of evidence. Investigators can often start with just an entity name, a formation email, a phone number, or a law firm address.

##### The Search Surfaces

We query Sixtyfour proprietary identity databases, dark web sources, and underground marketplace mentions in parallel. We check breach records for compromised emails, phones, and password reuse. We scan social profiles across LinkedIn, GitHub, Reddit, and Telegram memberships. We pull clear web public records including LLC registries, court filings, and news mentions.

##### Resolving the Graph

A flagged phone number surfaces in a 2023 breach record tied to an email. The same email is the registered owner of two other corporate entities under different names. The output returns a mapped visual of connected accounts, confidence scores per edge, and the exact surfaced identifiers linking them.

#### The Future of AML Tools and Corporate Entity Structures

As FinCEN rolls out stricter beneficial ownership reporting, operators will rely even more heavily on synthetic identities and dark web nominee rentals.

The institutions that survive the next decade of enforcement will not be the ones that collect the most corporate formation documents. They will be the ones that can accurately trace the single human operator hiding behind the paper.

---

### Case Study: How Mercor Uses Sixtyfour to Find AI Training Experts

*How Sixtyfour helps Mercor find rare experts who can generate training problems current AI models cannot solve.*
Source: https://www.sixtyfour.ai/blog/case-study-how-mercor-uses-sixtyfour-to-find-ai-training-experts
Christopher Price · 2026-02-11 · Company

#### Case Study: Mercor + Sixtyfour

#### The Problem

To train GPT-5, OpenAI needed GPT-4 to be wrong.

This is how model improvement works: you need examples where the current model fails but a human expert succeeds. These failure cases become the training data that teaches the next model what it doesn't know. But GPT-4 is already good enough that most people can't consistently generate questions it cannot answer.

When an AI lab needs these rare failure cases in dermatology, investment banking, or competitive programming, they turn to Mercor. Mercor has to find the small number of people whose expertise exceeds what the models have learned from the internet's corpus.

#### What Mercor Does with Sixtyfour

Before Sixtyfour, Mercor's sourcing process could take weeks per search, often returning candidates who looked qualified on paper but couldn't actually stump the models. Sixtyfour changed this.

Sixtyfour's platform starts with targeted data sources. For dermatologists who might know of rare conditions that current state of the art models don't, Sixtyfour pulls from medical association directories. For algorithmic problem experts, Sixtyfour examines competitive programming leaderboards. For complex financial structures the model might not grasp, Sixtyfour identifies people who went from collegiate consulting clubs to senior positions at investment banks.

A name from these sources means nothing by itself. So Sixtyfour's enrichment agents recursively explore everything they can find. They start with an initial data point, read through every linked page, conduct additional searches based on what they discover, then read through those results, continuously branching out. From a single name and affiliation, the agents might traverse academic publications, find co-authors, explore their work, discover conference presentations, identify specialized forums they participate in, and build a complete picture of expertise that no single source contains.

This recursive scraping methodology means that even when requirements become extremely narrow, say the labs need not just a dermatologist, but one specializing in rare genetic skin conditions affecting fewer than a thousand people worldwide, Sixtyfour can still deliver. The agents keep searching, reading, and connecting dots until they find the three people on Earth who fit the criteria.

Mercor now creates qualified candidate lists in hours rather than weeks. More importantly, these candidates actually possess the capability Mercor needs: they can consistently generate problems the AI cannot solve.

#### Results

The process works. Mercor successfully delivers experts to AI labs who can generate questions and problems that current state of the art models cannot solve. These become part of the training regime for the next generation of models.

For Mercor, this has made an extremely difficult sourcing challenge both manageable and fast. The foundational model companies need these experts to push their models forward. Without them, the models plateau. Mercor can reliably deliver these experts because Sixtyfour's recursive methodology can identify and qualify the right kind of expertise from millions of potential candidates, regardless of how specific the search parameters become.

As models improve, fewer humans will be able to provide useful training signals. The pool shrinks with each generation. But Sixtyfour's approach ensures that as long as such experts exist, Mercor will find them.

---

### Case Study: How Conduit Unlocked the STR Market with Sixtyfour

*How Sixtyfour helped Conduit identify fragmented short-term rental management companies that traditional data vendors could not reach.*
Source: https://www.sixtyfour.ai/blog/case-study-how-conduit-unlocked-the-str-market-with-sixtyfour
Saarth Shah · 2026-02-11 · Company

Conduit, formerly HostAI, builds AI agents for real estate—specifically for short-term and long-term rental management companies. If you run dozens of listings across Airbnb, Vrbo, Booking.com, or Tripadvisor, and you use a property management system like Guesty, HostAway, or RentManager, Conduit can automate your entire operations stack.

But there was one major problem: Conduit's market is big but very fragmented and hard to reach.

#### The Discovery Problem

Many STR management companies operate behind listing platforms that actively block backlinks to company landing pages. Most of these companies aren't big or well-known, and they’re distributed globally. Traditional data vendors like Apollo, ZoomInfo, BuiltWith, and Clay couldn’t help. After trying all of them, Conduit was left with only 500 qualified leads—nowhere near the volume needed to run effective outbound.

They even tried niche data providers. Still, the results were either duplicates, stale, or completely off-target.

Conduit was stuck. Until they found Sixtyfour.

#### How Sixtyfour Cracked the Code

At Sixtyfour, our AI agents are trained to find the kind of businesses that don’t show up on conventional databases. We have access to proprietary data and are designed to pull insights from every possible public source.

Within two weeks of working together, we helped Conduit do what no other vendor could: identify every Guesty, HostAway, and RentManager customer on the market.

Let’s break it down for just Guesty:

- BuiltWith results: ~728 leads, only ~400–500 qualified.

- Sixtyfour results: 8,000+ Guesty customers, with over 3,000 matching Conduit’s ICP, meaning companies with 25+ listings.

That’s a 10x leap in lead volume—and far higher quality.

#### The Enrichment Advantage

The next challenge: even with account names, traditional enrichment tools couldn’t surface real contact information.

Conduit tried running the list through Apollo and only got contact data for about 25% of the accounts.

Using Sixtyfour's enrichment API, we were able to surface verified leads for over 75% of those accounts—including many hard-to-find international businesses where the only mention of the owner might be buried in some obscure PDF or a two-year-old press article.

#### The Outcome

With high-quality leads flowing into their pipeline, Conduit is now on track to grow faster than ever before. They estimate:

- 30% month-over-month revenue growth.

- 3x growth over the next year.

By removing the biggest friction in their sales process—finding the right customers—Conduit is now free to focus on what they do best: building intelligent agents that automate away the headaches of property management.

This is exactly why we built Sixtyfour—to help companies find the hard-to-find. If you’re targeting niche, fragmented, or global markets that traditional tools can’t reach, let’s talk.

Because if the data exists on the internet, we’ll find it.

---

### Case Study: How Whatnot Discovered European TCG Sellers with Sixtyfour

*How Sixtyfour helped Whatnot identify qualified TCG sellers across the UK, Netherlands, and France for live auction commerce.*
Source: https://www.sixtyfour.ai/blog/case-study-how-whatnot-discovered-european-tcg-sellers-with-sixtyfour
Christopher Price · 2026-02-11 · Company

#### The Problem

Whatnot needed to find TCG sellers in the UK, Netherlands, and France who could anchor live auction streams. Not just any sellers, but ones with established audiences, consistent inventory, and proven selling ability.

Many doubted such sellers existed in these markets at scale. The assumption was that international TCG commerce happened primarily through local shops and traditional marketplaces, not social media. Finding social media creators who were also serious card sellers seemed unlikely, especially outside major cities.

#### What Whatnot Does with Sixtyfour

Sixtyfour proved the sellers existed. The platform identified social media accounts across Europe that showed Pokemon, Magic, and other TCG content, then recursively researched each one to determine commercial viability.

From a single seller handle, Sixtyfour's agents would uncover the complete seller profile: their eBay store showing monthly transaction volume, their TILT link revealing all selling channels, their Instagram with business contact information, their participation in card grading services. The agents distinguished between casual collectors making content and serious sellers moving real volume.

This recursive research transformed sparse social media presence into actionable intelligence. A Dutch social media account with 3,000 followers might turn out to be moving €10,000 monthly in graded Pokemon cards. A French creator doing pack openings might have a warehouse full of vintage inventory. These sellers existed, but only comprehensive research could identify them.

Whatnot's AEs used Sixtyfour's agents to generate pre-qualified lists with complete profiles: verified sales history, inventory depth, audience metrics, and direct contact information. Every account found was exactly who Whatnot needed to bring to the platform.

#### Results

Sixtyfour identified hundreds of qualified TCG sellers across European social media that no one believed existed at this scale. The platform found serious card sellers in Netherlands suburbs, vintage Pokemon dealers in French cities, and Magic the Gathering specialists throughout the UK, all with existing social audiences ready for live commerce.

What seemed impossible to find manually, Sixtyfour delivered systematically. Whatnot could enter European markets knowing exactly which sellers to recruit, with all the context needed to close them.

---

### Sixtyfour AI is now backed by Y Combinator

*Sixtyfour AI is joining Y Combinator’s first-ever spring batch as we go all in on building the best AI research platform in the world.*
Source: https://www.sixtyfour.ai/blog/sixtyfour-ai-is-now-backed-by-y-combinator
Saarth Shah · 2025-04-02 · Company

#### What is Y Combinator?

Y Combinator is the world’s most selective startup accelerator, with an acceptance rate under 1%. Since 2005, YC has funded over 5,000 companies—including Airbnb, Dropbox, Stripe, and Coinbase—whose combined portfolio valuation now exceeds $800 billion.

But YC’s real power is in the community. The alumni network is one of the strongest out there, filled with some of the most ambitious and impactful founders in the world.

#### What YC Means to Us

Between the two of us, Chris and I had applied to YC 10 times. We were always close—never quite made it. I first applied as a freshman in college, full of energy but nowhere near ready. I actually heard about YC back in middle school in India. To me, it symbolized the American Dream.

Over the years, we tried dozens of ideas, worked at YC companies, and kept learning. We never gave up, even when it felt like we were always one step away.

This time, it just clicked and we are beyond excited to be part of YC’s first-ever spring batch.

#### How Sixtyfour AI Started

Sixtyfour started like most good ideas do—half out of frustration, half out of curiosity.

It was January. I was at Whatnot (YC W20), and Chris had just taken time off from school. We’d both built startups before, and we knew how painful customer discovery was. Identifying the right leads felt broken. So much of it relied on tedious, manual research.

We had a hunch: what if we could automate that grind?

On our very first call, one of our friends—a founder at a VC-backed startup—paid us to help them find individual psychiatrists in specific geographies. That’s when it clicked: tools like Apollo and ZoomInfo didn’t have solid data on niche, local businesses. Building those lead lists meant scraping directories and manually verifying info online.

So we hacked together a prototype that could run “deep research” on any business or person—pulling together everything publicly available.

It worked. Within weeks, we helped that customer generate ~$5,000 in new revenue, with only $300 in costs on our end. We weren’t making money yet, but the signal was clear: this was valuable.

From there, we started aiming higher—going after companies with larger deal sizes and more complex workflows. That’s where our product really shines.

Right around then, the YC interview email landed—midway through a customer call. Twelve hours later, Chris had flown in from Texas and was crashing on my floor in Berkeley. We walked out of the interview thinking we bombed it. Two days later, we were apartment-hunting in San Francisco, and I had submitted my resignation at Whatnot. We were in.

#### The Road Ahead

Now with YC behind us, we’re going all in on building the best AI Research platform in the world.

It started with an enrichment API. Today, it’s become a full-blown research engine that can surface intel on nearly any person or business. We might be the only platform that can reliably identify tiny, obscure businesses—ones with just a couple employees in some corner of the world—and pull up real, actionable contact info. If it exists online, we can find it.

In the last two months alone, we’ve grown revenue 20x. But more importantly, we’re generating serious pipeline for our customers—and seeing even more value being built on top of our API.

And that’s just the beginning.

One use case we’re especially hyped about: finding every customer of legacy software companies. With vertical AI startups rapidly disrupting these niches, that data is turning out to be gold. If that’s something you’re working on, we’d love to talk.

#### Thank You

We wouldn’t be here without the early believers—our first customers, friends, and mentors who took a bet on us when all we had was a scrappy prototype and a strong conviction.

We’re still that same team, two founders who just wanted to build something useful—something people want and love. And we’re more excited than ever.

Thank you to everyone who’s been part of the journey so far. We’re just getting started.

#### Building something that needs better sales intelligence?

We’d love to help—reach out here or DM me on Twitter.

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